Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement
statistical models for ecological and environmental data analysis. We open the black box …

Researchers studying the evolution of viral pathogens and other organisms increasingly
encounter and use large and complex data sets from multiple different sources. Statistical …

We propose discrete Langevin proposal (DLP), a simple and scalable gradient-based
proposal for sampling complex high-dimensional discrete distributions. In contrast to Gibbs …

There is a lack of methodological results to design efficient Markov chain Monte Carlo
(MCMC) algorithms for statistical models with discrete-valued high-dimensional parameters …

Optimal scaling has been well studied for Metropolis-Hastings (MH) algorithms in
continuous spaces, but a similar understanding has been lacking in discrete spaces …

Learning-based methodologies increasingly find applications in safety-critical domains like
autonomous driving and medical robotics. Due to the rare nature of dangerous events, real …

Gradient-based sampling algorithms have demonstrated their effectiveness in text
generation, especially in the context of controlled text generation. However, there exists a …

Summary Hamiltonian Monte Carlo has emerged as a standard tool for posterior
computation. In this article we present an extension that can efficiently explore target …

We make use of Google search data in an attempt to predict unemployment, CPI and
consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries …

Abstract Hamiltonian Monte Carlo (HMC) is a successful approach for sampling from
continuous densities. However, it has difficulty simulating Hamiltonian dynamics with non …